Tag: Syrotuck

The C4ISR Journal had a recent search theory article quoting me along with Larry Stone.

The C4ISR Journal had a recent search theory article quoting me along with Larry Stone. I'm quite honored, like the British company in Dirk Gently's Holistic Detective Agency:

...was the only British software company that could be mentioned in the same sentence as ... Microsoft.... The sentence would probably run along the lines of ‘...unlike ... Microsoft...’ but it was a start.

It's a good article, covering the undeniably exciting historical origins hunting U-boats, and looking at what may be a modern renaissance. I think the article stretches to connect search theory with Big Data, but the author does note that when the data is visual, and you have humans scanning it for objects, there is a connection. With planning, it could have been used to prioritize the Amazon Mechanical Turk search for Jim Gray. (The resolution of the actual images in that search was probably too low regardless, but the core idea was sound.)

Syrotuck's main study is his 1976, with N=242. But he gives much more detail about distance travelled in his 1975 paper, breaking distance down every 0.2 miles. Unfortunately, he only reports probabilities, not numbers, and doesn't even report total N. We know he got more data between 1975 and 1976, but didn't know how much. Is the 1975 breakdown representative of the 1976 data? Unfortunately, no one has Syrotuck's original data. But we re-created it. (Spreadsheets available!)

Early 2003: Charles Twardy plans to reanalyze the Virginia data, correcting for some problems in last year's run. In February, we will analyze the Australian data for the draft report.

Dec 2001: In preparation for the Australian data, Adam Golding analyzed the Virginia data. Cluster analysis revealed only 4 or 5 types of lost person, assuming Gaussian (bell-shaped curve) types.

Adam Golding and Luke Hope then tested several machine-learned models, Syrotuck's model, and a simple model estimated by Rik Head. There were strong differences in predictive accuracy, but negligible differences in a more meaningful score, information reward. The most recent presentation of this work was in Charles Twardy's presentation to the NASAR 2002 conference in Charlotte, NC (June 2002).